Defect data enhancement and anomaly detection methods for smart solar photovoltaic maintenance

Targeting the poor precision, limited real-time and high model complexity of defects and exotic objects detection in solar photovoltaic panels, a new intelligent detection algorithm, SPP YOLO, is proposed. Expanded solar photovoltaic panels data from StyleGAN2-ADA. Building upon the YOLOv11 architecture, the proposed SPP YOLO method integrates Dynamic Snake Convolution (DSC) operations within the backbone’s CBS modules, resulting in the formation of DBS modules that leverage adaptive convolutional processing. By enhancing the global feature focus, this integration preserves the key information related to different global morphologies and improves the precision of target detection in the model. In addition, the coordinate attention mechanism is integrated into the C3K2 module to enhance the spatial perception of the model and reduce feature duplication. The use of the lightweight upsampling operator CARAFE in the feature extraction network allows contextual information to be collected across a wide range of sensory domains, improving the feature extraction and fusion capabilities of the model. A learning rate optimisation strategy based on Sparrow search algorithm (SSA) is used during model training to further improve the detection accuracy of the model. The proposed SPP YOLO algorithm, which helps to achieve a better balance between efficiency and accuracy in solar panel inspection, shows significant overall effectiveness and provides theoretical support for industrial smart manufacturing.